Microsoft Technology Licensing, LLC

Chiu Ying Cheung

Amir Netz

Giovanni Moises Della-Libera

Kevin Grealish

David John Nettleton

Andrew James Peacock

Taurean Addair Jones

Michael Justin Flasko

Christina Storm

Mark Wade Heninger

Cheryl Couris

Sonia P Carlson

Paula M Bach

Sumit Gulwani

Vu Minh Le

Ranvijay Kumar

Described herein are various technologies pertaining to construction and application of a data cleaning template. A data cleaning tool, when applying the data cleaning template to a data set, is configured to identify a column in the data set that has data entries of a data type specified in the data cleaning template. In response to identifying the column in the data set, the data cleaning tool, when applying the data cleaning template to the data set, alters a format of the data entries in the column from a first format to a second format, the second format specified in the data cleaning template.

Construction and application of data cleaning templates

Various technologies pertaining to extracting data encoded in a tree-structured document and generating a table based upon the extracted data are described herein. In a first embodiment, the table is generated without requiring input from a data cleaner. In a second embodiment, the table is generated based upon examples set forth by a data cleaner.

Generating tables based upon data extracted from tree-structured documents

Job execution can be scheduled and monitored. Execution of a job results in generation of one or more data slices, which can be consumed as input by one or more related jobs. Data slices can be presented in an interactive view. Selection of a data slice in the view can trigger automatic identification of related data slices based on dependencies between data slices. Each data slice can also include and be presented with a status indicating the availability of corresponding data, such as pending, ready, or failed. Furthermore, selection of a data slice that failed to generate successfully can trigger identification of related data slices that failed or are predicted to fail to generate successfully.

Job scheduling and monitoring in a distributed computing environment

A view of data transformation jobs can be presented by way of a user interface. Related jobs can subsequently be identified automatically after a job is selected based on data dependencies between jobs. Execution status can also be determined and presented such that successful and failed execution of jobs, for example, can be differentiated. Furthermore, selection of a job run that failed to execute successfully can trigger identification of related jobs runs that failed or are predicted to fail to execute successfully.

Growing community of inventors

Redmond, WA, United States of America

Chiu Ying Cheung